Species traits are associated with the quality of bioclimatic models

Aim To analyse the effects of nine species trait variables on the accuracy of bioclimatic envelope models built for 98 butterfly species. Location Finland, northern Europe. Methods Data from a national butterfly atlas monitoring scheme (NAFI) collected from 1991-2003 with a resolution of 10 x 10 km were used in the analyses. Generalized additive models (GAMs) were constructed for 98 butterfly species to predict their occurrence as a function of climatic variables. Modelling accuracy was measured as the cross-validation area under the curve (AUC) of the receiver-operating characteristic plot. Observed variation in modelling accuracy was related to species traits using multiple GAMs. The effects of phylogenetic relatedness among butterflies were accounted for by using generalized estimation equations. Results The values of the cross-validation AUC for the 98 species varied between 0.56 and 1.00 with a mean of 0.79. Five species trait variables were included in the GAM that explained 71.4% of the observed variation in modelling accuracy. Four variables remained significant after accounting for phylogenetic relatedness. Species with high mobility and a long flight period were modelled less accurately than species with low mobility and a short flight period. Large species (> 50 mm in wing span) were modelled more accurately than small ones. Species inhabiting mires had especially poor models, whereas the models for species inhabiting rocky outcrops, field verges and open fells were more accurate compared with other habitats. Main conclusions These results draw attention to the importance of species traits variables for species-climate impact models. Most importantly, species traits may have a strong impact on the performance of bioclimatic envelope models, and certain trait groups can be inherently difficult to model reliably. These uncertainties should be taken into account by downweighting or excluding species with such traits in studies applying bioclimatic modelling and making assessments of the impacts of climate change.